Our presentation is divided into three main chapters: (1) One chapter is dedicated to the problem of defining stochastic models for image subband data. We present a Markov Random Field (MRF) model for this data as well as an algorithm for, given a set of observations, identifying parameters of an MRF likely to have generated the observations. Image subbands have been empirically found not to behave like iid fields: our model captures this property by decomposing the field into the product of two fields, one markovian and stationary, the other independent and non-stationary. (2) Another chapter is dedicated to the problem error-resilient coding of images. We present data compression algorithms whose most salient feature is that their performance degrades gracefully in the presence of erasures: the quality of the decoded images depends only on the amount of data available for decoding, but not on which specific portions of the encoded bit stream are available. These coders represent a significant improvement over the state-of-the-art in the field. (3) A third chapter is dedicated to network modeling and control problems. Our most important contribution is the definition of a network/coder interface for IP networks which gathers channel state information, and then sets parameters of the video coder to maximize the quality of the signal delivered to the receiver, while remaining fair to other data or video connections: this interface plays a role analogous to that of a Leaky Bucket controller, in that it specifies traffic shape parameters which result in simultaneous good Quality of Service (QoS) for the source and good network performance. Experimental studies reveal that the proposed system is able to stream TV-broadcast quality video signals, among hosts in wide-area networks connected to the experimental vBNS backbone.